[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DTMTDP01YT15VZRA6NZAWX":3,"skill-same:01M6DTMTDP01YT15VZRA6NZAWX":119},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":48,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M6DTMTDP01YT15VZRA6NZAWX","spatial-genes",null,"Load when ranking spatially variable genes with Moran's I, SpatialDE, SPARK-X, or FlashS. Skip when detecting tissue domains (use spatial-domains) or differential expression between groups (use spatial-de).","TianGzlab\u002FOmicsClaw","TianGzlab","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F150978281?v=4","01M6DT9WNJJ470ADHPSMFAA02Y","01M6DT9WSN09C5K65GB6RK18R9",161,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"Spatial","spatial genes","spark","use","pattern","autocorrelation","SVG","expression","ranking","de","domains","spatially-variable-genes","morans-i","spatialde","sparkx","flashs","2026-11-10 10:12:30",false,"---\nname: spatial-genes\ndescription: Load when ranking spatially variable genes with Moran's I, SpatialDE, SPARK-X, or FlashS. Skip when detecting tissue domains (use spatial-domains) or differential expression between groups (use spatial-de).\ntrigger: spatially variable gene, spatial gene, SVG, SpatialDE, SPARK-X, spatial pattern, Moran, spatial autocorrelation\ntags:\n- spatial\n- svg\n- spatially-variable-genes\n- morans-i\n- spatialde\n- sparkx\n- flashs\n---\n\n# spatial-genes\n\n## When to use\n\nRank genes whose expression varies across spatial coordinates. Moran's I\nuses continuous log-normalized expression; SpatialDE, SPARK-X and the legacy\nFlashS approximation use counts. Scores have method-specific meanings.\n\n## Use from a step\n\n```python\nfrom skills._sdk.notebook import load_skill, read_input, write_output\nlibrary = load_skill(\"spatial-genes\")\nadata = read_input(\"processed.h5ad\")\nlibrary.spatial_genes(adata, random_state=0)\nwrite_output(library.results(adata), \"tables\u002Fsvg_results.csv\")\n```\n\nThe executable example checks spatial autocorrelation in simulated stripes.\n\n## API\n\n\u003C!-- api:begin generated from _api.py; regenerate with run.py api \u003Cskill dir> --write -->\n\n### `spatial_genes(adata, *, method='morans', n_top_genes=20, fdr_threshold=0.05, random_state=None, **parameters)`\n\nCompute spatial gene scores and return the same AnnData.\n\nMoran's I reads X; count-based methods prefer counts, then raw, then X.\nSpatialDE AEH does not expose a seed: results vary between runs.\n\n:param adata: Expression and spatial coordinates; modified in place.\n:param method: CLI default morans; spatialde, sparkx or flashs also supported.\n:param n_top_genes: CLI default 20 reported significant genes.\n:param fdr_threshold: CLI default 0.05 significance threshold.\n:param random_state: None uses CLI seeds, 0 for Moran's I and 42 for FlashS.\n:param parameters: Method-specific CLI parameters in references\u002Fparameters.md.\n:returns: The same AnnData with spatial_genes_results in uns.\n:raises ValueError: Method, thresholds or spatial coordinates are invalid.\n:raises ImportError: A backend is missing; use install_skill_deps.\n\n### `results(adata, *, significant_only=False)`\n\nReturn native spatial-gene scores and significance columns.\n\n:param adata: AnnData returned by spatial_genes.\n:param significant_only: Default False; True selects the run's FDR threshold.\n:returns: A new DataFrame; score meaning depends on the method.\n:raises ValueError: No run is recorded.\n\n### `run_info(adata, *, keep=True)`\n\nRead the most recent spatial-gene diagnostics.\n\n:param adata: AnnData returned by spatial_genes.\n:param keep: Default True; False removes transient diagnostics for CLI output.\n:returns: Method, thresholds and significant-gene counts.\n:raises ValueError: No run is recorded.\n\n### `ranking_figure(adata, *, n_top=20)`\n\nPlot the highest-scoring genes without writing files.\n\n:param adata: AnnData returned by spatial_genes.\n:param n_top: Default 20 genes, matching the CLI report size.\n:returns: A matplotlib Figure.\n:raises ValueError: No run is recorded or n_top is not positive.\n\n\u003C!-- api:end -->\n\n## Methods and parameters\n\nMoran's I defaults to six neighbors and 100 permutations. The default seed is\n0 for Moran's I and 42 for FlashS, matching their CLIs. SpatialDE's optional\nAEH clustering has no seed interface and may vary between runs. SPARK-X\nrequires R and SPARK. See [parameters](references\u002Fparameters.md) for backend\nkeywords and [methodology](references\u002Fmethodology.md) for algorithms.\n\n## Gotchas\n\n- `spatial_genes` stores every method's table in `uns['spatial_genes_results']`;\n  Moran's I also writes `uns['moranI']`.\n- `results` returns native scores, not a common calibrated statistic.\n- Count methods prefer `layers['counts']`, then raw, then X with a warning;\n  preserve original counts before normalization.\n- `run_info()['significance_column']` names the method's p-value\u002Fq-value column.\n- `spatial_genes(method='spatialde')` needs both SpatialDE and NaiveDE.\n\n## Inputs and outputs\n\nFunctions modify AnnData in place and return tables\u002FFigures without file\noutput. The CLI writes `processed.h5ad`, `tables\u002Fsvg_results.csv`, diagnostics,\nreport and result JSON. The [output contract](references\u002Foutput_contract.md)\ndistinguishes temporary R exchange files from delivered artifacts.\n\n## CLI\n\n```bash\npython skills\u002Fspatial\u002Fspatial-genes\u002Fspatial_genes.py --input processed.h5ad --output results\u002Fgenes\npython skills\u002Fspatial\u002Fspatial-genes\u002Fspatial_genes.py --demo --output \u002Ftmp\u002Fspatial-genes_demo\n```\n\n## See also\n\nUse spatial-preprocess to prepare expression, spatial-de to compare groups,\nand spatial-statistics for spatial relationships between labels.\n\n## Dependencies\n\n`anndata`, `matplotlib`, `numpy`, `pandas`, `scanpy`, `scipy`, `seaborn`, `SpatialDE`, `squidpy`, `statsmodels`\n\nSpatialDE also imports NaiveDE. SPARK-X requires the R package SPARK.\n","# spatial-genes\n\n## When to use\n\nRank genes whose expression varies across spatial coordinates. Moran's I\nuses continuous log-normalized expression; SpatialDE, SPARK-X and the legacy\nFlashS approximation use counts. Scores have method-specific meanings.\n\n## Use from a step\n\n```python\nfrom skills._sdk.notebook import load_skill, read_input, write_output\nlibrary = load_skill(\"spatial-genes\")\nadata = read_input(\"processed.h5ad\")\nlibrary.spatial_genes(adata, random_state=0)\nwrite_output(library.results(adata), \"tables\u002Fsvg_results.csv\")\n```\n\nThe executable example checks spatial autocorrelation in simulated stripes.\n\n## API\n\n\u003C!-- api:begin generated from _api.py; regenerate with run.py api \u003Cskill dir> --write -->\n\n### `spatial_genes(adata, *, method='morans', n_top_genes=20, fdr_threshold=0.05, random_state=None, **parameters)`\n\nCompute spatial gene scores and return the same AnnData.\n\nMoran's I reads X; count-based methods prefer counts, then raw, then X.\nSpatialDE AEH does not expose a seed: results vary between runs.\n\n:param adata: Expression and spatial coordinates; modified in place.\n:param method: CLI default morans; spatialde, sparkx or flashs also supported.\n:param n_top_genes: CLI default 20 reported significant genes.\n:param fdr_threshold: CLI default 0.05 significance threshold.\n:param random_state: None uses CLI seeds, 0 for Moran's I and 42 for FlashS.\n:param parameters: Method-specific CLI parameters in references\u002Fparameters.md.\n:returns: The same AnnData with spatial_genes_results in uns.\n:raises ValueError: Method, thresholds or spatial coordinates are invalid.\n:raises ImportError: A backend is missing; use install_skill_deps.\n\n### `results(adata, *, significant_only=False)`\n\nReturn native spatial-gene scores and significance columns.\n\n:param adata: AnnData returned by spatial_genes.\n:param significant_only: Default False; True selects the run's FDR threshold.\n:returns: A new DataFrame; score meaning depends on the method.\n:raises ValueError: No run is recorded.\n\n### `run_info(adata, *, keep=True)`\n\nRead the most recent spatial-gene diagnostics.\n\n:param adata: AnnData returned by spatial_genes.\n:param keep: Default True; False removes transient diagnostics for CLI output.\n:returns: Method, thresholds and significant-gene counts.\n:raises ValueError: No run is recorded.\n\n### `ranking_figure(adata, *, n_top=20)`\n\nPlot the highest-scoring genes without writing files.\n\n:param adata: AnnData returned by spatial_genes.\n:param n_top: Default 20 genes, matching the CLI report size.\n:returns: A matplotlib Figure.\n:raises ValueError: No run is recorded or n_top is not positive.\n\n\u003C!-- api:end -->\n\n## Methods and parameters\n\nMoran's I defaults to six neighbors and 100 permutations. The default seed is\n0 for Moran's I and 42 for FlashS, matching their CLIs. SpatialDE's optional\nAEH clustering has no seed interface and may vary between runs. SPARK-X\nrequires R and SPARK. See [parameters](references\u002Fparameters.md) for backend\nkeywords and [methodology](references\u002Fmethodology.md) for algorithms.\n\n## Gotchas\n\n- `spatial_genes` stores every method's table in `uns['spatial_genes_results']`;\n  Moran's I also writes `uns['moranI']`.\n- `results` returns native scores, not a common calibrated statistic.\n- Count methods prefer `layers['counts']`, then raw, then X with a warning;\n  preserve original counts before normalization.\n- `run_info()['significance_column']` names the method's p-value\u002Fq-value column.\n- `spatial_genes(method='spatialde')` needs both SpatialDE and NaiveDE.\n\n## Inputs and outputs\n\nFunctions modify AnnData in place and return tables\u002FFigures without file\noutput. The CLI writes `processed.h5ad`, `tables\u002Fsvg_results.csv`, diagnostics,\nreport and result JSON. The [output contract](references\u002Foutput_contract.md)\ndistinguishes temporary R exchange files from delivered artifacts.\n\n## CLI\n\n```bash\npython skills\u002Fspatial\u002Fspatial-genes\u002Fspatial_genes.py --input processed.h5ad --output results\u002Fgenes\npython skills\u002Fspatial\u002Fspatial-genes\u002Fspatial_genes.py --demo --output \u002Ftmp\u002Fspatial-genes_demo\n```\n\n## See also\n\nUse spatial-preprocess to prepare expression, spatial-de to compare groups,\nand spatial-statistics for spatial relationships between labels.\n\n## Dependencies\n\n`anndata`, `matplotlib`, `numpy`, `pandas`, `scanpy`, `scipy`, `seaborn`, `SpatialDE`, `squidpy`, `statsmodels`\n\nSpatialDE also imports NaiveDE. SPARK-X requires the R package SPARK.\n",{"name":5,"tags":39,"trigger":42,"description":7},[40,41,29,30,31,32,33],"spatial","svg","spatially variable gene, spatial gene, SVG, SpatialDE, SPARK-X, spatial pattern, Moran, spatial autocorrelation","main","90a3bec3e6549bc33dddbf67e4b23869736c49d1","skills\u002Fspatial\u002Fspatial-genes",27,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw","https:\u002F\u002FTianGzlab.github.io\u002FOmicsClaw\u002F","https:\u002F\u002Fgithub.com\u002FTianGzlab","2026-10-08 01:36:11",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add TianGzlab\u002FOmicsClaw --skill spatial-genes",{"tool":57,"command":58},"bunx","bunx skills add TianGzlab\u002FOmicsClaw --skill spatial-genes",{"tool":60,"command":61},"pnpm","pnpm dlx skills add TianGzlab\u002FOmicsClaw --skill spatial-genes","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002FSKILL.md",true,[65,71,75,81,85,91,95,99,103,107,111,115],{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"_api.py","SCRIPT","脚本",4685,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002F_api.py",{"path":72,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":73,"sourceHtmlUrl":74},"examples\u002Fexample_step.py",838,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002Fexamples\u002Fexample_step.py",{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"OPERATOR.json","REFERENCE","参考文档",528,"https:\u002F\u002Fgithub.com\u002Fhefarica\u002Farbitragex-v2\u002Fblob\u002Fmain\u002Fskills\u002Farbitragex-ultra\u002Foperators\u002Fop_32\u002FOPERATOR.json",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"r_visualization\u002FREADME.md",1613,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002Fr_visualization\u002FREADME.md",{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"r_visualization\u002Fsvg_publication_template.R","OTHER","其他",4190,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002Fr_visualization\u002Fsvg_publication_template.R",{"path":92,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fmethodology.md",9891,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002Freferences\u002Fmethodology.md",{"path":96,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Foutput_contract.md",1702,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002Freferences\u002Foutput_contract.md",{"path":100,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fparameters.md",3731,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002Freferences\u002Fparameters.md",{"path":104,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":105,"sourceHtmlUrl":106},"spatial_genes.py",48293,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002Fspatial_genes.py",{"path":108,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":109,"sourceHtmlUrl":110},"tests\u002F__init__.py",0,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002Ftests\u002F__init__.py",{"path":112,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":113,"sourceHtmlUrl":114},"tests\u002Ftest_api.py",950,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002Ftests\u002Ftest_api.py",{"path":116,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":117,"sourceHtmlUrl":118},"tests\u002Ftest_spatial_genes.py",6099,"https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-genes\u002Ftests\u002Ftest_spatial_genes.py",[120,138,149,166,191,214],{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":131,"publishedAt":34},"01M6DTP4X225Q92RMY8XXHPCTT","demo-skill","A fixture skill for the skill environment tests. Use it for nothing else.","https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Ftests\u002Fskillenv\u002Ffixtures\u002Fskills\u002Fdemo\u002Fdemo-skill\u002FSKILL.md",[126],{"categoryNo":127,"categoryName":128,"taxonomyType":129,"taxonomyTypeDesc":130},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[132,133,134,135,136,21,137],"demo skill","demo","tests","fixture","fixture skill","environment",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":144,"publishedAt":34},"01M6DTP28JTWQK3HHXDNQV8BBV","fake-domains","Test fixture for the golden deployment in tests\u002Fentry. Load never; it has no script.","https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Ftests\u002Fentry\u002Ffake_skills\u002Fspatial\u002Ffake-domains\u002FSKILL.md",[],[28,145,146,134,135,147,148],"fake domains","deployment","never","test",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":156,"publishedAt":34},"01M6DTNZ4AMWKTME1T18K5C643","replace-me-skill","Load when copying this directory to bootstrap a new OmicsClaw skill. Skip when an existing skill already covers the request.","https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Ftemplates\u002Fskill\u002FSKILL.md",[155],{"categoryNo":127,"categoryName":128,"taxonomyType":129,"taxonomyTypeDesc":130},[157,158,159,160,161,162,163,164,165],"me","replace me skill","template","directory","skill template","existing skill","new skill","bootstrap","scaffold",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":172,"publishedAt":34},"01M6DTNV63MTD0Q6YRC52F0B1Y","spatial-velocity","Load when estimating RNA velocity on a spatial AnnData with `layers[\"spliced\"]` + `layers[\"unspliced\"]` via scVelo (stochastic \u002F deterministic \u002F dynamical) or veloVI (deep generative). Skip when input lacks the spliced\u002Funspliced layers (must be quantified upstream by velocyto \u002F kb-python \u002F STARsolo); non-spatial scRNA velocity (use sc-velocity).","https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-velocity\u002FSKILL.md",[],[173,174,18,175,176,177,178,179,180,181,182,183,184,185,186,187,188,21,189,190],"velocity","spatial velocity","scvelo","input","starsolo","time","deep","RNA","kb-python","via","velocyto","rna-velocity","velovi","dynamics","upstream","kb","python","deterministic",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":197,"publishedAt":34},"01M6DTNPMR227NAT8QFY620YDW","spatial-trajectory","Load when inferring pseudotime \u002F lineage trajectories on a preprocessed spatial AnnData via DPT (default — diffusion pseudotime), CellRank (terminal-state + fate-probability), or Palantir (waypoint branch probabilities). Skip when the data has spliced\u002Funspliced layers and you want velocity-driven dynamics (use spatial-velocity); non-spatial scRNA pseudotime (use sc-pseudotime).","https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-trajectory\u002FSKILL.md",[],[18,198,199,200,201,202,203,204,182,205,206,207,208,209,173,210,21,211,212,213],"trajectory","spatial trajectory","state","terminal","lineage","diffusion","cellrank","palantir","dpt","pseudotime","probability","cell","default","branch","anndata","data",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":220,"publishedAt":34},"01M6DTNJEYFT33P84DGHDNXW95","spatial-statistics","Load when running spatial autocorrelation \u002F hotspot \u002F co-occurrence \u002F neighbourhood-enrichment \u002F Ripley K stats on a clustered spatial AnnData via squidpy. Skip when ranking spatially variable genes (use spatial-genes); tissue domain detection (use spatial-domains).","https:\u002F\u002Fgithub.com\u002FTianGzlab\u002FOmicsClaw\u002Fblob\u002Fmain\u002Fskills\u002Fspatial\u002Fspatial-statistics\u002FSKILL.md",[],[221,222,18,182,223,224,225,226,227,228,28,26,23,229,230,21,212,231,232,233],"spatial statistics","statistics","nhood-enrichment","co-occurrence","ripley","geary","moran","neighbourhood","local","domain","detection","enrichment","stats"]